Wind turbine malfunction early warning method based on cloud platform

Abstract

The invention discloses a wind turbine malfunction early warning method based on a cloud platform, directed at problems of traditional malfunction early warning patterns in wind turbines, such as limited data storage and transmission, insufficient computing capability and unbalanced computing loads. The method involves a data distributed storage center, a malfunction early warning center, a remote monitoring center, a malfunction early warning algorithm database based on Map-Reduce and a central monitoring chamber. According to the invention, the method can sufficiently conduct data mining on the huge amount of and multi-directional monitoring data of wind turbines, and at the same time provides early stage malfunction early warning services to a plurality of wind fields. The method of the invention realizes large scale data distributed storage and remote rapid reading, performs trend analysis, service life estimation and data mining by using omnibearing states monitoring data of the wind turbines, and realizes automatic early stage malfunction early warning of the wind turbines. The method is characterized by automatic identification, smart control, convenience and speediness, high efficiency, and low cost.

Description

technical field [0001] The invention relates to the field of fault early warning and maintenance of power generation equipment, in particular to a cloud platform-based fault early warning method for wind turbine groups. Background technique [0002] As of September 2014, the cumulative installed capacity of wind power in my country reached 98.588 million kilowatts, and the power generation exceeded that of nuclear power in the same period for two consecutive years. As the country with the largest wind power installed capacity in the world, the operation of wind farms is still based on post-failure alarms and maintenance. From the perspective of long-term safe production and economic operation of wind farms, fault warning should not be a "fault verdict" for equipment, but must be Wind turbines carry out all-round condition monitoring to achieve early warning and fault diagnosis, so as to effectively reduce economic losses and downtime caused by equipment damage. [0003] At ...

AI Technical Summary

Problems solved by technology

[0004] 1) Big data storage is limited, and early fault diagnosis cannot be realized

[0005] The traditional data storage and reading mode is completed by a single server, which does not have large data storage capabilities. Even if the wind farm is equipped with vibration measuring points, it is impossible to achieve long-term storage of vibration data and comprehensive analysis and comparison of vibration data of each unit in the entire wind farm. , and it is impossible to realize early fault diagnosis, and can only make judgments on the faults that have already occurred

[0006] 2) Insufficient computing power

[0007] Both the unit status trend analysis and the fault feature extraction and diagnosis involve large-scale data processing. The traditional single-computer calculation cannot meet the real-time needs, and when multiple wind turbines send out fault diagnosis requests at the same time, the external server of the wind farm and the remote center Servers have communication and overload issues

[0008] 3) The system load is unbalanced

[0009] The computer resources of the fault diagnosis system are unbalanced, mainly reflected in the tight storage and computing load of the server, while other computer resources are relatively idle, and the advantages of computers and networks cannot be maximized

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